In general, increasing the number of measurements decreases the resulting P value. P values are based on the difference between population means (or other summary metrics) as well as the number of measurements used to determine that difference. In this paper, we specifically address simple ways to communicate reproducibility when performing statistical tests and plotting data.Р”В®rror bars and P values are often used to assure readers of a real and persistent difference between populations or treatments. For excellent practical guides to statistics for cell biologists, readers are referred to Lamb et al, (2008) and Pollard et al. The resulting P values are worse than useless: counting each cell as a separate n can easily result in false-positive rates of >50% ( Aarts et al., 2015).

In the case of treating each cell as an n, the assumption that is violated is independent sampling, not necessarily the null hypothesis. But a small P value does not actually tell us which assumption is incorrect, the null hypothesis or some other assumption of the statistical model (e.g., normal distribution, random sampling, equal variance, etc.). A P value reports the probability that the observed data-or any more extreme values-would occur by chance (the РІР‚Сљnull hypothesisРІР‚Сњ). The P value should be treated as a mere heuristic, interpreted as the degree of compatibility between the observed dataset and a given statistical model. While far from perfect, the P value offers a pragmatic metric to infer whether an observed difference is reproducible and substantial relative to the noise in the measurements ( Greenwald et al., 1996).

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